Intel Xeon Scalable 2 x Intel Xeon Platinum 8380 testing with a Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite. ,,"Xeon 8380 2P" Processor,,2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads) Motherboard,,Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS) Chipset,,Intel Device 0998 Memory,,512GB Disk,,3841GB Micron_9300_MTFDHAL3T8TDP + 250GB SABRENT Graphics,,ASPEED Monitor,,VE228 Network,,2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP OS,,Ubuntu 22.04 Kernel,,5.15.0-48-generic (x86_64) Desktop,,GNOME Shell 42.4 Display Server,,X Server 1.21.1.3 Vulkan,,1.2.204 Compiler,,GCC 11.2.0 File-System,,ext4 Screen Resolution,,1920x1080 ,,"Xeon 8380 2P" "NAMD - ATPase Simulation - 327,506 Atoms (days/ns)",LIB,0.29175 "OpenFOAM - Input: drivaerFastback, Small Mesh Size - Mesh Time (sec)",LIB,29.241586 "OpenFOAM - Input: drivaerFastback, Small Mesh Size - Execution Time (sec)",LIB,57.552839 "LAMMPS Molecular Dynamics Simulator - Model: 20k Atoms (ns/day)",HIB,32.777 "LAMMPS Molecular Dynamics Simulator - Model: Rhodopsin Protein (ns/day)",HIB,30.653 "srsRAN - Test: OFDM_Test (Samples / Second)",HIB,128300000 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM (eNb Mb/s)",HIB,287.3 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM (UE Mb/s)",HIB,117.9 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM (eNb Mb/s)",HIB,226.9 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM (UE Mb/s)",HIB,114.5 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM (eNb Mb/s)",HIB,310.9 "srsRAN - Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM (UE Mb/s)",HIB,123.4 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM (eNb Mb/s)",HIB,266.6 "srsRAN - Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM (UE Mb/s)",HIB,140.5 "srsRAN - Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM (eNb Mb/s)",HIB,109.6 "srsRAN - Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM (UE Mb/s)",HIB,68.8 "AOM AV1 - Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K (FPS)",HIB,27.21 "AOM AV1 - Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K (FPS)",HIB,17.76 "AOM AV1 - Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K (FPS)",HIB,31.08 "AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K (FPS)",HIB,35.33 "AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K (FPS)",HIB,35.45 "AOM AV1 - Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p (FPS)",HIB,51.89 "AOM AV1 - Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p (FPS)",HIB,44.45 "AOM AV1 - Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p (FPS)",HIB,57.49 "AOM AV1 - Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p (FPS)",HIB,64.71 "AOM AV1 - Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p (FPS)",HIB,62.84 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,2.223 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,68.849 "SVT-AV1 - Encoder Mode: Preset 10 - Input: Bosphorus 4K (FPS)",HIB,119.356 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,176.554 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,5.973 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,137.096 "SVT-AV1 - Encoder Mode: Preset 10 - Input: Bosphorus 1080p (FPS)",HIB,303.961 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,456.595 "7-Zip Compression - Test: Compression Rating (MIPS)",HIB,333614 "7-Zip Compression - Test: Decompression Rating (MIPS)",HIB,364594 "Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,28.73 "Timed Linux Kernel Compilation - Build: allmodconfig (sec)",LIB,233.927 "Timed Node.js Compilation - Time To Compile (sec)",LIB,155.084 "Timed PHP Compilation - Time To Compile (sec)",LIB,42.772 "Y-Cruncher - Pi Digits To Calculate: 1B (sec)",LIB,10.442 "Y-Cruncher - Pi Digits To Calculate: 10B (sec)",LIB,116.863 "Y-Cruncher - Pi Digits To Calculate: 500M (sec)",LIB,5.21 "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,1.57678 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,2.15621 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.82992 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.615814 "oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,4.82537 "oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,2.40843 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,1.41063 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,7.12496 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,0.88619 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.13953 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.365597 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.220123 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,743.288 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,510.8 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,747.336 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,2.11079 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,4.24112 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,3.63234 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,563.009 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,0.235167 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,749.783 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,502.424 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.176383 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,9.13339 "ASTC Encoder - Preset: Fast (MT/s)",HIB,808.7029 "ASTC Encoder - Preset: Medium (MT/s)",HIB,325.8134 "ASTC Encoder - Preset: Thorough (MT/s)",HIB,45.7167 "ASTC Encoder - Preset: Exhaustive (MT/s)",HIB,4.5344 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,22.9 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,242.42 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,23.21 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,1705.46 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,13.39 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,2943.06 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,13.09 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,3014.96 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,1049.24 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,38.06 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,85 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,469.6 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,4468.14 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,8.93 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,2447.12 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,32.61 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,243.86 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,163.56 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,8919.96 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,8.94 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,2162.24 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,18.45 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,47277.73 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,1.53 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,80215.33 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,0.84